Internalized Heterosexism, Social Support, and Career Development in Lesbian, Gay, and Bisexual Undergraduate and Graduate Students: An Application of Social Cognitive Career Theory
Bibliographic record
Abstract
Using a the framework of Social Cognitive Career Theory (Lent, Brown, & Hackett, 1994), we examined the relationships between one potential career-related barrier, internalized heterosexism (IH), and social support on career decision-making self-efficacy (CDMSE) and vocational outcome expectations in lesbian, gay, and bisexual undergraduate and graduate students. Specifically, we predicted that internalized heterosexism would be negatively related to CDMSE and vocational outcome expectations, and that social support would serve as a buffer that moderates these relationships. Results indicated that IH and social support were both unique predictors of outcome expectations. There was also a significant interaction effect between IH and social support in relation to vocational outcome expectations, such that for those with lower levels of social support, there was a significant, positive relationship between IH and outcome expectations, whereas for those with higher levels of social support, there was no significant relationship between IH and outcome expectations. Social support was also significantly related to CDMSE, but neither IH, nor the interaction of social support and IH were significantly related to CDMSE. The implications are discussed within the context of the bottleneck hypothesis and competing psychological demands (e.g., Hetherington, 1991).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".